All for One, One for All: Interdisciplinary Collaboration in the Treatment of Addictions
Bibliographic record
Abstract
In the treatment of addictions, the contribution of psychosocial practitioners in an interdisciplinary team is significant. Effective public health interventions have been led by concerted health professionals. Screening for potentially addictive practices is necessary not only in emergency rooms and in the offices of general practitioners but also within mental health and addiction services albeit the resistance of both medical and non-medical practitioners to systematically implement such screening procedures. Temperament and personality assessment can help establish more tailored treatment plans. Given that when treatments are compared with each other the difference in outcomes is typically small and variable it is suggested that successful interdisciplinary teams share facilitative interpersonal skills. A group of concerned and competent practitioners that complement their knowhow in an interdisciplinary team may be the optimal solution to provide the help needed by patients throughout the course of recovery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".